402 Monitoring Threatened Species and Ecological Communities
broadly inform the value for conservation of alternative cutting regimes to
conventional clearfelling operations (Gustafsson et al. 2012; Lindenmayer et al.
2012a; Fedrowitz et al. 2014).
In 2009, the Black Saturday wildfires burned half of the 28 sites in the AM
experiment and the subsequent loss of statistical power precluded the maintenance
of the project in its original form, although the sites were subsequently
incorporated in a study of post-fire (salvage) logging effects on forest birds
(Lindenmayer et al. unpublished data) and plants (Blair et al. 2016).
Discussion
The case study from the mountain ash forests has some important general lessons
for both threatened species monitoring and AM. Some of these are brief ly outlined
in the remainder of this chapter.
Adaptive management for threatened species may not be the best approach for
achieving a conservation outcome
We st gate et al. (2013) demonstrated that AM studies on threatened species are
extremely rare. It often can be hard to manipulate the populations of, or habitat for,
threatened species, especially when very few individuals remain, or the total
distribution is very small. It may be impossible to design experiments with
replicates of one or more types of management interventions to give sufficient
statistical power for robust inference. In the case of the mountain ash forests,
current timber harvesting prescriptions should (at least in theory) preclude logging
of suitable habitat for Leadbeater’s possum (Lindenmayer et al. 2013). Therefore,
the variable retention harvesting experiment could not legally include potential
habitat for Leadbeater’s possum; the primary species for which alternative
silvicultural systems were designed to benefit could not be part of the AM
experiment. In situations such as this, alternative approaches are often required to
gather the knowledge needed to improve management actions and conservation
policies. These may include space-for-time observation studies, simulation
modelling or other forms of investigation. However, it remains imperative that
there are robust pathways to continue to improve knowledge and improve
management actions based on that new knowledge.
Build adaptive management on existing resource management operations to
limit costs of establishing and maintaining them
AM experiments can be complex and costly to implement and maintain, thereby
limiting their instigation in the first place. One way to mitigate these issues is to
‘piggyback’ them on existing natural resource management activities, thereby
reducing costs and logistical effort (Walters 1986). For example, the AM program
for the malleefowl is ‘piggybacked’ on the national monitoring program for the